How to Build an ETL App for NetSuite Data in Python with CData



Create ETL applications and real-time data pipelines for NetSuite data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for NetSuite and the petl framework, you can build NetSuite-connected applications and pipelines for extracting, transforming, and loading NetSuite data. This article shows how to connect to NetSuite with the CData Python Connector and use petl and pandas to extract, transform, and load NetSuite data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live NetSuite data in Python. When you issue complex SQL queries from NetSuite, the driver pushes supported SQL operations, like filters and aggregations, directly to NetSuite and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

About NetSuite Data Integration

CData provides the easiest way to access and integrate live data from Oracle NetSuite. Customers use CData connectivity to:

  • Access all editions of NetSuite, including Standard, CRM, and OneWorld.
  • Connect with all versions of the SuiteTalk API (SOAP-based) and SuiteQL, which functions like SQL, enabling easier data querying and manipulation.
  • Access predefined and custom reports through support for Saved Searches.
  • Securely authenticate with Token-based and OAuth 2.0, ensuring compatibility and security for all use cases.
  • Use SQL stored procedures to perform functional actions like uploading or downloading files, attaching or detaching records or relationships, retrieving roles, getting extra table or column info, getting job results, and more.

Customers use CData solutions to access live NetSuite data from their preferred analytics tools, Power BI and Excel. They also use CData's solutions to integrate their NetSuite data into comprehensive databases and data warehouse using CData Sync directly or leveraging CData's compatibility with other applications like Azure Data Factory. CData also helps Oracle NetSuite customers easily write apps that can pull data from and push data to NetSuite, allowing organizations to integrate data from other sources with NetSuite.

For more information about our Oracle NetSuite solutions, read our blog: Drivers in Focus Part 2: Replicating and Consolidating ... NetSuite Accounting Data.


Getting Started


Connecting to NetSuite Data

Connecting to NetSuite data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The User and Password properties, under the Authentication section, must be set to valid NetSuite user credentials. In addition, the AccountId must be set to the ID of a company account that can be used by the specified User. The RoleId can be optionally specified to log in the user with limited permissions.

See the "Getting Started" chapter of the help documentation for more information on connecting to NetSuite.

After installing the CData NetSuite Connector, follow the procedure below to install the other required modules and start accessing NetSuite through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for NetSuite Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.netsuite as mod

You can now connect with a connection string. Use the connect function for the CData NetSuite Connector to create a connection for working with NetSuite data.

cnxn = mod.connect("Account Id=XABC123456;Password=password;User=user;Role Id=3;Version=2013_1;")

Create a SQL Statement to Query NetSuite

Use SQL to create a statement for querying NetSuite. In this article, we read data from the SalesOrder entity.

sql = "SELECT CustomerName, SalesOrderTotal FROM SalesOrder WHERE Class_Name = 'Furniture : Office'"

Extract, Transform, and Load the NetSuite Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the NetSuite data. In this example, we extract NetSuite data, sort the data by the SalesOrderTotal column, and load the data into a CSV file.

Loading NetSuite Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'SalesOrderTotal')

etl.tocsv(table2,'salesorder_data.csv')

In the following example, we add new rows to the SalesOrder table.

Adding New Rows to NetSuite

table1 = [ ['CustomerName','SalesOrderTotal'], ['NewCustomerName1','NewSalesOrderTotal1'], ['NewCustomerName2','NewSalesOrderTotal2'], ['NewCustomerName3','NewSalesOrderTotal3'] ]

etl.appenddb(table1, cnxn, 'SalesOrder')

With the CData Python Connector for NetSuite, you can work with NetSuite data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for NetSuite to start building Python apps and scripts with connectivity to NetSuite data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.netsuite as mod

cnxn = mod.connect("Account Id=XABC123456;Password=password;User=user;Role Id=3;Version=2013_1;")

sql = "SELECT CustomerName, SalesOrderTotal FROM SalesOrder WHERE Class_Name = 'Furniture : Office'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'SalesOrderTotal')

etl.tocsv(table2,'salesorder_data.csv')

table3 = [ ['CustomerName','SalesOrderTotal'], ['NewCustomerName1','NewSalesOrderTotal1'], ['NewCustomerName2','NewSalesOrderTotal2'], ['NewCustomerName3','NewSalesOrderTotal3'] ]

etl.appenddb(table3, cnxn, 'SalesOrder')

Ready to get started?

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